{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation and Improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# create a synthetic dataset\n",
    "X, y = make_blobs(random_state=0)\n",
    "# split data and labels into a training and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "# instantiate a model and fit it to the training set\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "# evaluate the model on the test set\n",
    "print(\"Test set score: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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KuWOLk2/EHFmhwGHQTHHWXwHWqMmyWcggSYYhzn2je5QKjajKeSsVwg+ToRH1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\nZm8gNDUgMCBSID4+CnN0YXJ0eHJlZgoxMTY4OQolJUVPRgo=\n",
      "image/png": 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8kZKOKLy0AChemv/Uws/ba6x+K/C8PXYR8e2IODAiDo2IQ4HtEfG889nqkdrG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\nMzMzMzOzRPx/lR5f0TgpvtcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddeb2c53c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-Validation in scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [ 0.961  0.922  0.958]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "scores = cross_val_score(logreg, iris.data, iris.target)\n",
    "print(\"Cross-validation scores: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [ 1.     0.967  0.933  0.9    1.   ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
    "print(\"Cross-validation scores: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cross-validation score: 0.96\n"
     ]
    }
   ],
   "source": [
    "print(\"Average cross-validation score: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benefits of Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified K-Fold cross-validation and other strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris labels:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(\"Iris labels:\\n{}\".format(iris.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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KZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDI0NSAvRmls\ndGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFULuNQzEM6z0FFwhg/Sx7nndIldu/PUpGcIUh\nWj+SWhKYiMBLDLGUb+JHRkE9C78XheIzxM8XhUHOhKRAnPUZEJl4htpGbuh2cM68wzOMOQIXxVpw\nptOZ9lzY5JwHJxDObZTxjEK6SVQVcVSfcUzxqrLPjdeBpbVss9OR7CGNhEtJJSaXflMq/7QpWyro\n2kUTsEjkgZNNNOEsP0OSYsyglFH3MLWO9HGykUd10MnZnDktmdnup+1MfA9YJplR5Smd5zI+J6nz\nXE597rMd0eSipVX7nP3ekZbyIrXbodXpVyVRmY3Vp5C4PP+Mn/H+A46gWT4KZW5kc3RyZWFtCmVu\nZG9iagozNiAwIG9iago8PCAvTGVuZ3RoIDEzMyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJl\nYW0KeJxNj0ESwzAIA+9+hZ6AsQHznnR6Sv5/LZA27gXtjICRhjAIPGIM6zAlvHr74VWkS3A2jvkl\nGUU8CGoL3BdUBUdjip342N2h7KXi6RRNi+sRc9O0pHQ3USptvZ3I+MB9n94fVbYknYIeW+qELtEk\n8kUCc9hUMM/qxktLj6ft2d4fZj4z1wplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9MZW5n\ndGggMzM4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEVSS3LFMAjb5xRcIDPmZ+Pz\nvE5X6f23lXA63Tz0DAgJMj1lSKbcNpZkhOQc8qVXZIjVkJ9GjkTEEN8pocCu8rm8lsRcyG6JSvGh\nHT+XpTcyza7QqrdHpzaLRjUrI+cgQ4R6VujM7lHbZMPrdiHpOlMWh3As/0MFspR1yimUBG1B39gj\n6G8WPBHcBrPmcrO5TG71v+5bC57XOluxbQdACZZz3mAGAMTDCdoAxNza3hYpKB9VuopJwq3yXCc7\nULbQqnS8N4AZBxg5YMOSrQ7XaG8Awz4P9KJGxfYVoKgsIP7O2WbB3jHJSLAn5gZOPXE6xZFwSTjG\nAkCKreIUuvEd2OIvF66ImvAJdTplTbzCntrix0KTCO9ScQLwIhtuXR1FtWxP5wm0PyqSM2KkHsTR\nCZHUks4RFJcG9dAa+7iJGa+NxOaevt0/wjmf6/sXFriD4AplbmRzdHJlYW0KZW5kb2JqCjM4IDAg\nb2JqCjw8IC9MZW5ndGggMTYzIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQuXUE\nMQxDc1WBEniAOuoZP0ez/acLabzeQPp4hHiIPQnDcl3FhdENP962zDS8jjLcjfVlxviosUBO0AcY\nIhNXo0n17YozVOnh1WKuo6JcLzoiEsyS46tAI3w6ssdDW9uZfjqvf+wh7xP/KirnbmEBLqruQPlS\nH/HUj9lR6pqhjyorax5q2r8IuyKUtn1cTmWcunsHtMJnK1f7fQOo5zqACmVuZHN0cmVhbQplbmRv\nYmoKMzkgMCBvYmoKPDwgL0xlbmd0aCA2OCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0K\neJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohbQjRBlIJYEKVmJmYQ\nSTgDIpcGAMm0FeUKZW5kc3RyZWFtCmVuZG9iago0MCAwIG9iago8PCAvTGVuZ3RoIDQ1IC9GaWx0\nZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXJYQVi4XTCwH\nzALRlnAKIp4GAJ99DLUKZW5kc3RyZWFtCmVuZG9iago0MSAwIG9iago8PCAvTGVuZ3RoIDE2MSAv\nRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkEsSwyAMQ/ecQkfwRwZ8nnS6Su+/rSFN\ns4CnsUAGdycEqbUFE9EFL21Lugs+WwnOxnjoNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwutEx1m\nDnqUoxmgEDoV3u2i5HKm7s75R3D1X/VHse6czcTAZOUOhGb1Ke58mx1RXd1kf9JjbtZrfxX2qrC0\nrKXlhNvOXTOgBO6pHO39BalzOoQKZW5kc3RyZWFtCmVuZG9iago0MiAwIG9iago8PCAvTGVuZ3Ro\nIDIxNCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9ULsRQzEI6z0FC+TOfO03z8ul\ny/5tJJykQjZCEpSaTMmUhzrKkqwpTx0+S2KHvIflbmQ2JSpFL5OwJffQCvF9ieYU993VlrNDNJdo\nOX4LMyqqGx3TSzaacCoTuqDcwzP6DW10A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/61y91Lc\n7z0cb6KIlHTwrvnl9MvPLbxOPY5Eur35imtxpjoKRHBGavKKdGHFsshDpNUENT0Da7UArt56+Tdo\nR3QZgOwTieM0pRxD/9a4x+sDh4pS9AplbmRzdHJlYW0KZW5kb2JqCjQzIDAgb2JqCjw8IC9MZW5n\ndGggMjM2IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1QS25EIQzbc4pc4EkkIQHO\nQ9VV5/7bscNU7SqGGH9ID+myVR7rU2J1iezypU2XyjJ5FajlT9v/UQwCbv/QyEG0t4ydYuYS1sXC\nJDzlNCMbJ9csH487TxtmhcbEjeOdLhlgnxYBNVuVzYE5bTo3QLqQGreqs95kUAwi6kLNB5MunKfR\nl4g5nqhgSncmtZAbXD7VoQNxWr0KuWOLk2/EHFmhwGHQTHHWXwHWqMmyWcggSYYhzn2je5QKjajK\neSsVwg+ToRH1htWgBpW5haKp5ZL8HdoCMAW2jHXpDEqBqgDB3yqnfb8BJI1dUwplbmRzdHJlYW0K\nZW5kb2JqCjQ0IDAgb2JqCjw8IC9MZW5ndGggMTU3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0\ncmVhbQp4nEWQuRFDMQhEc1VBCRKwCOqxx9F3/6kX+Uq0bwAth68lU6ofJyKm3Ndo9DB5Dp9NJVYs\n2Ca2kxpyGxZBSjGYeE4xq6O3oZmH1Ou4qKq4dWaV02nLysV/82hXM5M9wjXqJ/BN6PifPLSp6Fug\nrwuUfUC1OJ1JUDF9r2KBo5x2fyKcGOA+GUeZKSNxYm4K7PcZAGa+V7jG4wXdATd5CmVuZHN0cmVh\nbQplbmRvYmoKNDUgMCBvYmoKPDwgL0xlbmd0aCAzMzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicLVI5jiQxDMv9Cn5gAOvy8Z4eTNT7/3RJVQUFqmzLPORyw0QlfiyQ21Fr4tdGZqDC\n8K+rzIXvSNvIOohryEVcyZbCZ0Qs5DHEPMSC79v4GR75rMzJswfGL9n3GVbsqQnLQsaLM7TDKo7D\nKsixYOsiqnt4U6TDqSTY44v/PsVzF4IWviNowC/556sjeL6kRdo9Ztu0Ww+WaUeVFJaD7WnOy+RL\n6yxXx+P5INneFTtCaleAojB3xnkujjJtZURrYWeDpMbF9ubYj6UEXejGZaQ4AvmZKsIDSprMbKIg\n/sjpIacyEKau6Uont1EVd+rJXLO5vJ1JMlv3RYrNFM7rwpn1d5gyq807eZYTpU5F+Bl7tgQNnePq\n2WuZhUa3OcErJXw2dnpy8r2aWQ/JqUhIFdO6Ck6jyBRL2Jb4moqa0tTL8N+X9xl//wEz4nwBCmVu\nZHN0cmVhbQplbmRvYmoKNDYgMCBvYmoKPDwgL0xlbmd0aCAxMzEgL0ZpbHRlciAvRmxhdGVEZWNv\nZGUgPj4Kc3RyZWFtCnicRY/LDQQhDEPvVOES8hk+qYfVntj+r+swmkFC+EEiO/EwCKzz8jbQxfDR\nosM3/jbVq2OVLB+6elJWD+mQh7zyFVBpMFHEhVlMHUNhzpjKyJYytxvhtk2DrGyVVK2DdjwGD7an\nZasIfqltYeos8QzCVV64xw0/kEutd71Vvn9CUzCXCmVuZHN0cmVhbQplbmRvYmoKNDcgMCBvYmoK\nPDwgL0xlbmd0aCA3MiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1jLERwDAIA3um\n0Ag2WGDvk0tF9m9DfE4DLx0Pl6LBWg26giNwdan80SNduSlFl2POguFxql9IMUY9qCPj3sdPuV9w\nFhJ9CmVuZHN0cmVhbQplbmRvYmoKNDggMCBvYmoKPDwgL0xlbmd0aCA4OCAvRmlsdGVyIC9GbGF0\nZURlY29kZSA+PgpzdHJlYW0KeJw1jLsRwDAIQ3tPwQgGi4/3yaVK9m+D7dCApHf3goM6QfK4Gymc\nLm7ZV3obj5OeJgCx9ExD7d9gRdWLWhQtX25j0GIqvj/6JCCWdfJeOPSQEt4fxRcdewplbmRzdHJl\nYW0KZW5kb2JqCjE0IDAgb2JqCjw8IC9UeXBlIC9Gb250IC9CYXNlRm9udCAvRGVqYVZ1U2FucyAv\nRmlyc3RDaGFyIDAgL0xhc3RDaGFyIDI1NQovRm9udERlc2NyaXB0b3IgMTMgMCBSIC9TdWJ0eXBl\nIC9UeXBlMyAvTmFtZSAvRGVqYVZ1U2FucwovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIz\nMyBdIC9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9DaGFyUHJvY3MgMTUgMCBS\nCi9FbmNvZGluZyA8PCAvVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ1\nIC9oeXBoZW4gNDggL3plcm8gL29uZSAvdHdvIC90aHJlZSAvZm91ciA1NCAvc2l4IDU2IC9laWdo\ndAo2NyAvQyAvRCA3MCAvRiA4MyAvUyAvVCA4NiAvViA5NyAvYSAvYiAvYyAvZCAvZSAvZiAvZyAv\naCAvaSAxMDggL2wgMTEwIC9uCi9vIC9wIDExNCAvciAvcyAvdCAxMTggL3YgL3cgXQo+PgovV2lk\ndGhzIDEyIDAgUiA+PgplbmRvYmoKMTMgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9G\nb250TmFtZSAvRGVqYVZ1U2FucyAvRmxhZ3MgMzIKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0\nIDEyMzMgXSAvQXNjZW50IDkyOSAvRGVzY2VudCAtMjM2IC9DYXBIZWlnaHQgMAovWEhlaWdodCAw\nIC9JdGFsaWNBbmdsZSAwIC9TdGVtViAwIC9NYXhXaWR0aCAxMzQyID4+CmVuZG9iagoxMiAwIG9i\nagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2\nMDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYw\nMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkw\nIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYK\nNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIg\nNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2\nMTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYz\nNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1\nIDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4Mzgg\nNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3\nMCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1\nMjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1\nMDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYg\nNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2\nODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4\nNyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEz\nIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzgg\nMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1\nOTIgNjM1IDU5MiBdCmVuZG9iagoxNSAwIG9iago8PCAvc3BhY2UgMTYgMCBSIC9oeXBoZW4gMTcg\nMCBSIC96ZXJvIDE4IDAgUiAvb25lIDE5IDAgUiAvdHdvIDIwIDAgUgovdGhyZWUgMjEgMCBSIC9m\nb3VyIDIyIDAgUiAvc2l4IDIzIDAgUiAvZWlnaHQgMjQgMCBSIC9DIDI1IDAgUiAvRCAyNiAwIFIK\nL0YgMjcgMCBSIC9TIDI4IDAgUiAvVCAyOSAwIFIgL1YgMzAgMCBSIC9hIDMxIDAgUiAvYiAzMiAw\nIFIgL2MgMzMgMCBSCi9kIDM0IDAgUiAvZSAzNSAwIFIgL2YgMzYgMCBSIC9nIDM3IDAgUiAvaCAz\nOCAwIFIgL2kgMzkgMCBSIC9sIDQwIDAgUgovbiA0MSAwIFIgL28gNDIgMCBSIC9wIDQzIDAgUiAv\nciA0NCAwIFIgL3MgNDUgMCBSIC90IDQ2IDAgUiAvdiA0NyAwIFIKL3cgNDggMCBSID4+CmVuZG9i\nagozIDAgb2JqCjw8IC9GMSAxNCAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBl\nIC9FeHRHU3RhdGUgL0NBIDAgL2NhIDEgPj4KL0EyIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEg\nL2NhIDEgPj4gPj4KZW5kb2JqCjQ5IDAgb2JqCjw8IC9UeXBlIC9QYXR0ZXJuIC9QYXR0ZXJuVHlw\nZSAxIC9QYWludFR5cGUgMSAvVGlsaW5nVHlwZSAxCi9CQm94IFsgMCAwIDcyIDcyIF0gL1hTdGVw\nIDcyIC9ZU3RlcCA3MgovUmVzb3VyY2VzIDw8IC9Qcm9jc2V0cyBbIC9QREYgL1RleHQgL0ltYWdl\nQiAvSW1hZ2VDIC9JbWFnZUkgXSA+PgovTWF0cml4IFsgMSAwIDAgMSAwIDM1MS4yNjg3NSBdIC9M\nZW5ndGggMTYyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nHWPMQ7DMAwDd72CH3Ah\nO4rirF2y9w9tl3jJ0u+XdOfChAFT1olyOM/jML+tXvf0rceC/4/rPRu2Jl1PvKziY1aWBDWMV/WO\nk46DGhaNTpPTAhSdjj1l1A5q2BrYfRr822iko4eMhKCZ2MTU5GG/2aclioqspGhsLLOZnSka0WXi\nyV5F4/AyA3B6iMd8ZWZUwBCT8cvcQ0sswtrdvkeQN8QKZW5kc3RyZWFtCmVuZG9iago1MCAwIG9i\nago8PCAvVHlwZSAvUGF0dGVybiAvUGF0dGVyblR5cGUgMSAvUGFpbnRUeXBlIDEgL1RpbGluZ1R5\ncGUgMQovQkJveCBbIDAgMCA3MiA3MiBdIC9YU3RlcCA3MiAvWVN0ZXAgNzIKL1Jlc291cmNlcyA8\nPCAvUHJvY3NldHMgWyAvUERGIC9UZXh0IC9JbWFnZUIgL0ltYWdlQyAvSW1hZ2VJIF0gPj4KL01h\ndHJpeCBbIDEgMCAwIDEgMCAzNTEuMjY4NzUgXSAvTGVuZ3RoIDE1MiAvRmlsdGVyIC9GbGF0ZURl\nY29kZSA+PgpzdHJlYW0KeJwljzEOwzAMA3e9gh8Q4DiK4qxduucPbZd4ydLvl1QhwoAp60Q3NNb5\ntAWq+1P3vUv3C2/6XzNfE9Q0HksbuOg0UNOi0+lyeoCiM3CkjGWAmrYFjlYG33Ya2TBCRkLQTOxi\navO0/+7LEq4mOykaB72GOZmiEe2FJ3sTjcu9AnB7iMd8XhkVMMRkfK9/6BOrsPawHyWzMRwKZW5k\nc3RyZWFtCmVuZG9iago1IDAgb2JqCjw8IC9IMSA0OSAwIFIgL0gyIDUwIDAgUiA+PgplbmRvYmoK\nNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCA+PgplbmRvYmoKMiAwIG9iago8PCAvVHlw\nZSAvUGFnZXMgL0tpZHMgWyAxMCAwIFIgXSAvQ291bnQgMSA+PgplbmRvYmoKNTEgMCBvYmoKPDwg\nL0NyZWF0b3IgKG1hdHBsb3RsaWIgMi4wLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1\nY2VyIChtYXRwbG90bGliIHBkZiBiYWNrZW5kKSAvQ3JlYXRpb25EYXRlIChEOjIwMTcwNjI3MTQ1\nNTE1LTA0JzAwJykKPj4KZW5kb2JqCnhyZWYKMCA1MgowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAw\nMDAwMTYgMDAwMDAgbiAKMDAwMDAxMzg5MyAwMDAwMCBuIAowMDAwMDEyODI3IDAwMDAwIG4gCjAw\nMDAwMTI4NTkgMDAwMDAgbiAKMDAwMDAxMzgwOCAwMDAwMCBuIAowMDAwMDEzODUxIDAwMDAwIG4g\nCjAwMDAwMTM4NzIgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzk2IDAwMDAw\nIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwMjI0NCAwMDAwMCBuIAowMDAwMDExMzkzIDAw\nMDAwIG4gCjAwMDAwMTExOTMgMDAwMDAgbiAKMDAwMDAxMDcyNiAwMDAwMCBuIAowMDAwMDEyNDQ2\nIDAwMDAwIG4gCjAwMDAwMDIyNjUgMDAwMDAgbiAKMDAwMDAwMjM1NCAwMDAwMCBuIAowMDAwMDAy\nNDc4IDAwMDAwIG4gCjAwMDAwMDI3NjEgMDAwMDAgbiAKMDAwMDAwMjkxMyAwMDAwMCBuIAowMDAw\nMDAzMjM0IDAwMDAwIG4gCjAwMDAwMDM2NDUgMDAwMDAgbiAKMDAwMDAwMzgwNyAwMDAwMCBuIAow\nMDAwMDA0MTk3IDAwMDAwIG4gCjAwMDAwMDQ2NjIgMDAwMDAgbiAKMDAwMDAwNDk2NyAwMDAwMCBu\nIAowMDAwMDA1MjAwIDAwMDAwIG4gCjAwMDAwMDUzNDYgMDAwMDAgbiAKMDAwMDAwNTc1NyAwMDAw\nMCBuIAowMDAwMDA1ODkzIDAwMDAwIG4gCjAwMDAwMDYwMzUgMDAwMDAgbiAKMDAwMDAwNjQxMiAw\nMDAwMCBuIAowMDAwMDA2NzIyIDAwMDAwIG4gCjAwMDAwMDcwMjUgMDAwMDAgbiAKMDAwMDAwNzMy\nNSAwMDAwMCBuIAowMDAwMDA3NjQzIDAwMDAwIG4gCjAwMDAwMDc4NDkgMDAwMDAgbiAKMDAwMDAw\nODI2MCAwMDAwMCBuIAowMDAwMDA4NDk2IDAwMDAwIG4gCjAwMDAwMDg2MzYgMDAwMDAgbiAKMDAw\nMDAwODc1MyAwMDAwMCBuIAowMDAwMDA4OTg3IDAwMDAwIG4gCjAwMDAwMDkyNzQgMDAwMDAgbiAK\nMDAwMDAwOTU4MyAwMDAwMCBuIAowMDAwMDA5ODEzIDAwMDAwIG4gCjAwMDAwMTAyMTggMDAwMDAg\nbiAKMDAwMDAxMDQyMiAwMDAwMCBuIAowMDAwMDEwNTY2IDAwMDAwIG4gCjAwMDAwMTI5NTggMDAw\nMDAgbiAKMDAwMDAxMzM4OCAwMDAwMCBuIAowMDAwMDEzOTUzIDAwMDAwIG4gCnRyYWlsZXIKPDwg\nL1NpemUgNTIgL1Jvb3QgMSAwIFIgL0luZm8gNTEgMCBSID4+CnN0YXJ0eHJlZgoxNDEwMQolJUVP\nRgo=\n",
      "image/png": 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ZmZmZmVl7K1eIj46Iu4cOTMPG1K1FZmZmZmZmZm2sXCG+WZlx3bVuiJmZmZmZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde7e60ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_stratified_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More control over cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[ 1.     0.933  0.433  0.967  0.433]\n"
     ]
    }
   ],
   "source": [
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "      cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[ 0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3)\n",
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[ 0.9   0.96  0.96]\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leave-one-out cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cv iterations:  150\n",
      "Mean accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"Number of cv iterations: \", len(scores))\n",
    "print(\"Mean accuracy: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shuffle-split cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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XjxdRFeb5IU+ih+lK4nw8KCFcezBGFhLkU9FAjrNcrfJeQvYOtxqywkFqSeez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\nPgpzdGFydHhyZWYKMTUyNzcKJSVFT0YK\n",
      "image/png": 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PP8+kSZN45JFHGD9+fMp55bp8S+oahfjTLZ+snh06dGDSpEnstddeKaf/3nvv8cgjj0S6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddeb2c5278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_shuffle_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[ 0.92   0.987  0.92   0.96   0.987  0.947  0.92   0.853  0.88   0.96 ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_splits=10)\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)\n",
    "print(\"Cross-validation scores:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross-validation with groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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zMwVzI0uFFEMuIwszoEAulwVYIIfL0NAQyjI2MVIwNDQFskzNjaFiMI1AWUuQ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\nMCBSID4+CnN0YXJ0eHJlZgoxNDM3NgolJUVPRgo=\n",
      "image/png": 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BvyYUZn2+PH/71ztXTBknJJJOC44XGSzptpTb/UCbu6MkjZI0ImXRJGBDys/n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde78afcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_group_kfold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[ 0.75   0.8    0.667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "# create synthetic dataset\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "# assume the first three samples belong to the same group,\n",
    "# then the next four, etc\n",
    "groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "scores = cross_val_score(logreg, X, y, groups, cv=GroupKFold(n_splits=3))\n",
    "print(\"Cross-validation scores:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple Grid-Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 112   size of test set: 38\n",
      "Best score: 0.97\n",
      "Best parameters: {'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "# naive grid search implementation\n",
    "from sklearn.svm import SVC\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,\n",
    "                                                    random_state=0)\n",
    "print(\"Size of training set: {}   size of test set: {}\".format(\n",
    "      X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters, train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the test set\n",
    "        score = svm.score(X_test, y_test)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "print(\"Best score: {:.2f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The danger of overfitting the parameters and the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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p7nybHVFd3WR/0mNu1mt/FfaqsLSspeWE285dM6AE7qkc7f0FqXM6hAplbmRzdHJlYW0K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\nIDAgUiAvSW5mbyAzOCAwIFIgPj4Kc3RhcnR4cmVmCjg5MzAKJSVFT0YK\n",
      "image/png": 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JRNwFXZ1PLf5JCpaKbF74e29SD9M7gfVzetcV0vMxM45wcGXM4DmdNAZ6yATs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde768f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_threefold_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84   size of validation set: 28   size of test set: 38\n",
      "\n",
      "Best score on validation set: 0.96\n",
      "Best parameters:  {'C': 10, 'gamma': 0.001}\n",
      "Test set score with best parameters: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# split data into train+validation set and test set\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)\n",
    "# split train+validation set into training and validation sets\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_trainval, y_trainval, random_state=1)\n",
    "print(\"Size of training set: {}   size of validation set: {}   size of test set:\"\n",
    "      \" {}\\n\".format(X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the validation set\n",
    "        score = svm.score(X_valid, y_valid)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "# rebuild a model on the combined training and validation set,\n",
    "# and evaluate it on the test set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)\n",
    "test_score = svm.score(X_test, y_test)\n",
    "print(\"Best score on validation set: {:.2f}\".format(best_score))\n",
    "print(\"Best parameters: \", best_parameters)\n",
    "print(\"Test set score with best parameters: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid-search with cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference: manual_grid_search_cv\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters,\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        # perform cross-validation\n",
    "        scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
    "        # compute mean cross-validation accuracy\n",
    "        score = np.mean(scores)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "# rebuild a model on the combined training and validation set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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yAyEM6zmFLpAZjG1gz7OZVC/3b59ksg0S/kjy9ERHJl7myAis2fG2FhmIGfgW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\nCjE3MjQ5CiUlRU9GCg==\n",
      "image/png": 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Pqls6B56eSv32t0rt3q0fEra8XP/v7t369Z6et27/4IP6cprAFh3juXPnqjZt2qiZM2da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\nTE1+WgdZiliGxtFcrUnUhJ4FPBoYfSL+LuCMovMcVqlGRjQ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yha8fEX9RoUa6XtFKS5iH\nYjFRv+ZhXV32fSuyk1haqjUZ47izvj8LeFFEXDbx+H7AKyOi+rUHLcgew6Vmc0Ws4Xzv9CVjOmDp\nFxTjNFY5Wsd6dqdXQB+Khft+21FnSSytxnFPfd+bS9/bGG6Nf17GLDZLn/IEOQlM0f7E3JQkFiVH\n/C14khTQT99n0jKJJXsMQ9MFcU9939VJxq3ThDLGcebvitOXjFlslv4aCg0pPBcDhwI7l9thwEXl\nudb1a/n6jwWOBq6TdLWGcxa+Djy6PFeLkxji/CYZRfwtCqcD+wPHA0eU2wnAfYF3Z7yABex7YFAd\nVru/APQyhqGvvrdLv21kjOOefleMMQ1ZeuVJc07hqenrj7XZUt3qQkvYCKrIovV9L/QyhidZ9L63\nS79tZIzjXn9XjDH1sfKUkMKzGi3Oh2j8hqIXLWHuqsgi9f08NKGG9DKGN2PR+z4irgN+ccKl/5Bd\n+plkjOMuf1eMMfVZeuWJlROT3yzpxeX2FgYN6hU1CmRHOzYmVUtoqFikqCId9X1P6kO6WrPgmlhq\n30fEORHxpnLzYmI2GePYGpoxZqtYeuUJUlJ4eop27E5LaKyIddH3PakPPY7hlvTU9z2RMY79u2KM\n2Vq8oEhgI/j6tWkZ8deTXtNL36vD02xbx1T2Mo577PueyIhbdaSrMWYtrDytQsUUntSTnzP0isZa\nQk96TS993536kKDWpI5j9/1ykqGIWUMzxqyFdyhWoVYKj+Z88vOi0ZNi0UvfW33YdnoZx+57Y4wx\na+EFRTKtfP1e9AroV7Hooe+tPmw9GePYfW+MMWYjsPTKU3YKT0Rc3yjesSdNqJckqc3ooe+tPmwT\nGePYfW+MMWbuLP0OhVN4Nh5WLLaNnvq+J5JSeNz3xhhj5o4PtoMDpyTtfAU4v5xNsCh0cwBRxgFX\nPSlidNT3PZF0UJv73hhjzNxZeuWJ5BSehnSXxOIkqa2mu77vicbj2H1vjDFm7lh5Sk7hkXRxRNxv\n1v11tGtNaBuYhyrivje1cd8bY4zZCCz9gmKclicmZ+Eklq2jxyQp9/3y4r43xhgzT7ygSKAzX78L\nysX4n4iIt048/lTgIbUuxnffG2OMMaZ3fA1FDj35+r3wu8CTJJ0r6bXl9kngKcBzK9Zx3xtjjDGm\na7xDkYCjHTcurVUR970xxhhjesexsTk42nGDEhHnMFyA3wr3vTHGGGO6xsrTGA1PTHa04/LivjfG\nGGNM11h5SsDRjsuL+94YY4wxvbP0C4rMFB5HOy4v7ntjjDHG9IoXFNJZwIsi4rKJx/cDXhkRR87n\nlRljjDHGGLPx8TUUw+7EZZMPlsf2yn85xhhjjDHGLA5eUDiFxxhjjDHGmO3GCwqn8BhjjDHGGLPd\n+BoKp/AYY4wxxhiz3Sz9gmKEU3iMMcYYY4zZdrygMMYYY4wxxmw3vobCGGOMMcYYs914QWGMMcYY\nY4zZbrygMMZURdIPJX1O0uWS/kbSzvN+TQCSXpxcb39JR4zdP1TSL47df4akYzNfkzHGGNMCLyiM\nMbX5bkTsHxH3YUhPe8bW/kNJt2r3stjmBcU6X8/+wBFj9w8FbllQRMRbIuJd62jfGGOM2RB4QWGM\nack/AXsDSDpT0kWSNkl6+ugLJH1H0mslXQLcX9Jxki4oOxynSlL5unMlvV7ShZKulHSwpL+TdI2k\nl4+193hJnym7JKdIupWkVwM7lcfeM+vrpr2e8W9G0nMkXSHpUknvLY/tIukdpa3PSnqUpNsALwOO\nKe2/gGFh9bxy/0GSjpf0+2Pf24mljaslPag8vrOk95Wa75f0aUkHle/pL8rP6DJJz2vTfcYYY8za\n7DjvF2CM6RNJOwIPBz5cHnpyRNwgaSfgAklnRMT1wC7ApyPi98q/uyIiXlb+fjrwK8AHSxvfj4iD\nJD0X+ABwIHAD8AVJrwfuCBwDPCAibpZ0MvC4iHihpGdHxP6l3X2mfR3wrsnXM8ELgbtHxPck7V4e\newnwiYh4cnnsM8A/AscBB0XEs0vNnYDvRMSflvuHT7S9Y0T8fNGkXgr8b+CZwDcjYl9J9wE+V752\nf+AuZReIsddijDHGpOMFhTGmNjtJGr3x/Sfg7eXvz5F0VPn7XYF7AdcDPwTOGPv3h0n6A2Bn4A7A\nJlYWFH9f/ryM4byYrwFIura0+UCGRcYFZWNjJ+AbU17j4at83eTrGedS4D2SzgTOLI89FHjkaLcB\nuB2w54x/vxp/V/68CNir/P2BwBsAIuJySZeWx68F7iHpTcCHgI9uRz1jjDGmCl5QGGNq893RTsAI\nSYcyfOJ+/4i4UdK5DG+8AW6KiB+Wr7sdcDLDJ/tflnT82NcBfK/8+aOxv4/u7wgIOC0iXrTGa1zt\n6255PVN4BPC/gCOBl0jar7T1axFx1cT3fMgar2GS0ffzQ9aYmyPim5LuCzyMQaV6DPDkbaxnjDHG\nVMHXUBhjMtiNQd25UdK9gV+Y8XWjxcN/StoVOHob63wcOFrSHQEk3UHS3cpzN0u69VZ83VQk7QDc\nNSLOAV5QvqddgY8AvzN2rccB5Z98G/ixsSYm728N/8ywWEDSvsB+5e8/AewQEWcAfwjcbxvbNcYY\nY6rhBYUxJoMPAztKuhJ4NXD+tC+KiP8C3gpczvBG/YJtKRIRVzC8wf5o0YM+Bty5PH0qcKmk96zx\ndbO4FfBuSZcBnwXeWF7vHwO3Lm1vKvcBzgH2LRdhH8OgbR01uih7K7+lk4GflHQF8HIG/etbwF2A\nc4ta9m5grR0ZY4wxphmKiHm/BmOMMVMoyVO3joibJN2T4WLvn4mI78/5pRljjDG34GsojDFm47Iz\ncE5RtQQ804sJY4wxGw3vUBhjjDHGGGO2G19DYYwxxhhjjNluvKAwxhhjjDHGbDdeUBhjjDHGGGO2\nGy8ojDHGGGOMMduNFxTGGGOMMcaY7cYLCmOMMcYYY8x28/8BB+ZNx8yKnfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde77357f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_val_selection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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vRW5jb2RpbmcgPDwgL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDMy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\nNiAwIFIgPj4Kc3RhcnR4cmVmCjkzNTcKJSVFT0YK\n",
      "image/png": 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E+fn5WLFiBbp3744NGzZYHF+V8bpPnz5YuXIlBg8ejE6dOsHDwwOPPfYY2rdvj0mTJmHS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde5106978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter grid:\n",
      "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "print(\"Parameter grid:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5,\n",
    "                          return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(grid_search.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 100, 'gamma': 0.01}\n",
      "Best cross-validation score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters: {}\".format(grid_search.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator:\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print(\"Best estimator:\\n{}\".format(grid_search.best_estimator_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Analyzing the result of cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>...</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.29e-03</td>\n",
       "      <td>5.38e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>2.53e-04</td>\n",
       "      <td>1.56e-04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.34e-03</td>\n",
       "      <td>3.56e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>6.04e-04</td>\n",
       "      <td>3.93e-05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.65e-03</td>\n",
       "      <td>9.80e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>8.47e-04</td>\n",
       "      <td>7.05e-04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>2.80e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>3.93e-05</td>\n",
       "      <td>8.16e-06</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.29e-03</td>\n",
       "      <td>4.01e-04</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>6.03e-04</td>\n",
       "      <td>1.66e-04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.85e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score  \\\n",
       "0       1.29e-03         5.38e-04             0.37              0.37   \n",
       "1       1.34e-03         3.56e-04             0.37              0.37   \n",
       "2       1.65e-03         9.80e-04             0.37              0.37   \n",
       "3       7.88e-04         2.80e-04             0.37              0.37   \n",
       "4       1.29e-03         4.01e-04             0.37              0.37   \n",
       "\n",
       "        ...        std_fit_time std_score_time std_test_score  std_train_score  \n",
       "0       ...            2.53e-04       1.56e-04           0.01         2.85e-03  \n",
       "1       ...            6.04e-04       3.93e-05           0.01         2.85e-03  \n",
       "2       ...            8.47e-04       7.05e-04           0.01         2.85e-03  \n",
       "3       ...            3.93e-05       8.16e-06           0.01         2.85e-03  \n",
       "4       ...            6.03e-04       1.66e-04           0.01         2.85e-03  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# convert to Dataframe\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# show the first 5 rows\n",
    "display(results.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fdde77b0eb8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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MD7Yuv7EiC611JezKmiCeK++hmbKx0djiYHAaJl6AFjdg6GmNGjV04YKmLpVCgcUl8Jl8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\n",
      "image/png": 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HlrSnV3rDF1sNQ3P52FJi46w89Vx0UFkAtnxUw7IF5XgNzbgJkUyZHrh+qpwGi+4sJW+v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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde763e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.array(results.mean_test_score).reshape(6, 6)\n",
    "\n",
    "# plot the mean cross-validation scores\n",
    "mglearn.tools.heatmap(scores, xlabel='gamma', xticklabels=param_grid['gamma'],\n",
    "                      ylabel='C', yticklabels=param_grid['C'], cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fdde7eda6a0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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KeJw1Ujmu3UAM630KXSCAds2c5wWpfu7fhpRfCkO0VoqajhaVafllIVUtky6/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\nMTI0MzYKJSVFT0YK\n",
      "image/png": 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b+O4/W/h88a8e23v2a8+Tb/4dW8oxADb+sItP314HQECQHzOfuZ7omFC01rw6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\ngBUBDADAigAGAGBFAAMAsBKN1Wq9H3vkBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde7444ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(13, 5))\n",
    "\n",
    "param_grid_linear = {'C': np.linspace(1, 2, 6),\n",
    "                     'gamma':  np.linspace(1, 2, 6)}\n",
    "\n",
    "param_grid_one_log = {'C': np.linspace(1, 2, 6),\n",
    "                      'gamma':  np.logspace(-3, 2, 6)}\n",
    "\n",
    "param_grid_range = {'C': np.logspace(-3, 2, 6),\n",
    "                    'gamma':  np.logspace(-7, -2, 6)}\n",
    "\n",
    "for param_grid, ax in zip([param_grid_linear, param_grid_one_log,\n",
    "                           param_grid_range], axes):\n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    scores = grid_search.cv_results_['mean_test_score'].reshape(6, 6)\n",
    "\n",
    "    # plot the mean cross-validation scores\n",
    "    scores_image = mglearn.tools.heatmap(\n",
    "        scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "        yticklabels=param_grid['C'], cmap=\"viridis\", ax=ax)\n",
    "\n",
    "plt.colorbar(scores_image, ax=axes.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of grids:\n",
      "[{'kernel': ['rbf'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}, {'kernel': ['linear'], 'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n"
     ]
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "               'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
    "              {'kernel': ['linear'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n",
    "print(\"List of grids:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "Best cross-validation score: 0.97\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5,\n",
    "                          return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(\"Best parameters: {}\".format(grid_search.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.00094</td>\n",
       "      <td>0.00094</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.00036</td>\n",
       "      <td>0.00036</td>\n",
       "      <td>0.00037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.00034</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.00045</td>\n",
       "      <td>0.00069</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.00021</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_C</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_gamma</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_kernel</th>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>...</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 100, 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.00012</td>\n",
       "      <td>0.00022</td>\n",
       "      <td>0.00057</td>\n",
       "      <td>0.00051</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4e-05</td>\n",
       "      <td>2.2e-05</td>\n",
       "      <td>1.9e-05</td>\n",
       "      <td>3.8e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>4.2e-05</td>\n",
       "      <td>0.00017</td>\n",
       "      <td>0.00018</td>\n",
       "      <td>0.00051</td>\n",
       "      <td>...</td>\n",
       "      <td>5.3e-05</td>\n",
       "      <td>5.9e-06</td>\n",
       "      <td>2.7e-05</td>\n",
       "      <td>2.6e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.0055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               0   \\\n",
       "mean_fit_time                                             0.00094   \n",
       "mean_score_time                                           0.00034   \n",
       "mean_test_score                                              0.37   \n",
       "mean_train_score                                             0.37   \n",
       "param_C                                                     0.001   \n",
       "param_gamma                                                 0.001   \n",
       "param_kernel                                                  rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "rank_test_score                                                27   \n",
       "split0_test_score                                            0.38   \n",
       "split0_train_score                                           0.36   \n",
       "split1_test_score                                            0.35   \n",
       "split1_train_score                                           0.37   \n",
       "split2_test_score                                            0.36   \n",
       "split2_train_score                                           0.37   \n",
       "split3_test_score                                            0.36   \n",
       "split3_train_score                                           0.37   \n",
       "split4_test_score                                            0.38   \n",
       "split4_train_score                                           0.36   \n",
       "std_fit_time                                              0.00012   \n",
       "std_score_time                                            4.2e-05   \n",
       "std_test_score                                              0.011   \n",
       "std_train_score                                            0.0029   \n",
       "\n",
       "                                                              1   \\\n",
       "mean_fit_time                                            0.00094   \n",
       "mean_score_time                                           0.0004   \n",
       "mean_test_score                                             0.37   \n",
       "mean_train_score                                            0.37   \n",
       "param_C                                                    0.001   \n",
       "param_gamma                                                 0.01   \n",
       "param_kernel                                                 rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}   \n",
       "rank_test_score                                               27   \n",
       "split0_test_score                                           0.38   \n",
       "split0_train_score                                          0.36   \n",
       "split1_test_score                                           0.35   \n",
       "split1_train_score                                          0.37   \n",
       "split2_test_score                                           0.36   \n",
       "split2_train_score                                          0.37   \n",
       "split3_test_score                                           0.36   \n",
       "split3_train_score                                          0.37   \n",
       "split4_test_score                                           0.38   \n",
       "split4_train_score                                          0.36   \n",
       "std_fit_time                                             0.00022   \n",
       "std_score_time                                           0.00017   \n",
       "std_test_score                                             0.011   \n",
       "std_train_score                                           0.0029   \n",
       "\n",
       "                                                             2   \\\n",
       "mean_fit_time                                            0.0013   \n",
       "mean_score_time                                         0.00045   \n",
       "mean_test_score                                            0.37   \n",
       "mean_train_score                                           0.37   \n",
       "param_C                                                   0.001   \n",
       "param_gamma                                                 0.1   \n",
       "param_kernel                                                rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                              27   \n",
       "split0_test_score                                          0.38   \n",
       "split0_train_score                                         0.36   \n",
       "split1_test_score                                          0.35   \n",
       "split1_train_score                                         0.37   \n",
       "split2_test_score                                          0.36   \n",
       "split2_train_score                                         0.37   \n",
       "split3_test_score                                          0.36   \n",
       "split3_train_score                                         0.37   \n",
       "split4_test_score                                          0.38   \n",
       "split4_train_score                                         0.36   \n",
       "std_fit_time                                            0.00057   \n",
       "std_score_time                                          0.00018   \n",
       "std_test_score                                            0.011   \n",
       "std_train_score                                          0.0029   \n",
       "\n",
       "                                                           3   \\\n",
       "mean_fit_time                                          0.0014   \n",
       "mean_score_time                                       0.00069   \n",
       "mean_test_score                                          0.37   \n",
       "mean_train_score                                         0.37   \n",
       "param_C                                                 0.001   \n",
       "param_gamma                                                 1   \n",
       "param_kernel                                              rbf   \n",
       "params              {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                            27   \n",
       "split0_test_score                                        0.38   \n",
       "split0_train_score                                       0.36   \n",
       "split1_test_score                                        0.35   \n",
       "split1_train_score                                       0.37   \n",
       "split2_test_score                                        0.36   \n",
       "split2_train_score                                       0.37   \n",
       "split3_test_score                                        0.36   \n",
       "split3_train_score                                       0.37   \n",
       "split4_test_score                                        0.38   \n",
       "split4_train_score                                       0.36   \n",
       "std_fit_time                                          0.00051   \n",
       "std_score_time                                        0.00051   \n",
       "std_test_score                                          0.011   \n",
       "std_train_score                                        0.0029   \n",
       "\n",
       "                                 ...                \\\n",
       "mean_fit_time                    ...                 \n",
       "mean_score_time                  ...                 \n",
       "mean_test_score                  ...                 \n",
       "mean_train_score                 ...                 \n",
       "param_C                          ...                 \n",
       "param_gamma                      ...                 \n",
       "param_kernel                     ...                 \n",
       "params                           ...                 \n",
       "rank_test_score                  ...                 \n",
       "split0_test_score                ...                 \n",
       "split0_train_score               ...                 \n",
       "split1_test_score                ...                 \n",
       "split1_train_score               ...                 \n",
       "split2_test_score                ...                 \n",
       "split2_train_score               ...                 \n",
       "split3_test_score                ...                 \n",
       "split3_train_score               ...                 \n",
       "split4_test_score                ...                 \n",
       "split4_train_score               ...                 \n",
       "std_fit_time                     ...                 \n",
       "std_score_time                   ...                 \n",
       "std_test_score                   ...                 \n",
       "std_train_score                  ...                 \n",
       "\n",
       "                                                38  \\\n",
       "mean_fit_time                               0.0004   \n",
       "mean_score_time                            0.00025   \n",
       "mean_test_score                               0.95   \n",
       "mean_train_score                              0.97   \n",
       "param_C                                        0.1   \n",
       "param_gamma                                    NaN   \n",
       "param_kernel                                linear   \n",
       "params              {'C': 0.1, 'kernel': 'linear'}   \n",
       "rank_test_score                                 11   \n",
       "split0_test_score                             0.96   \n",
       "split0_train_score                            0.97   \n",
       "split1_test_score                             0.91   \n",
       "split1_train_score                            0.98   \n",
       "split2_test_score                                1   \n",
       "split2_train_score                            0.94   \n",
       "split3_test_score                             0.91   \n",
       "split3_train_score                            0.98   \n",
       "split4_test_score                             0.95   \n",
       "split4_train_score                            0.97   \n",
       "std_fit_time                               8.4e-05   \n",
       "std_score_time                             5.3e-05   \n",
       "std_test_score                               0.033   \n",
       "std_train_score                              0.012   \n",
       "\n",
       "                                              39  \\\n",
       "mean_fit_time                            0.00036   \n",
       "mean_score_time                           0.0002   \n",
       "mean_test_score                             0.97   \n",
       "mean_train_score                            0.98   \n",
       "param_C                                        1   \n",
       "param_gamma                                  NaN   \n",
       "param_kernel                              linear   \n",
       "params              {'C': 1, 'kernel': 'linear'}   \n",
       "rank_test_score                                1   \n",
       "split0_test_score                              1   \n",
       "split0_train_score                          0.99   \n",
       "split1_test_score                           0.96   \n",
       "split1_train_score                          0.98   \n",
       "split2_test_score                              1   \n",
       "split2_train_score                          0.98   \n",
       "split3_test_score                           0.95   \n",
       "split3_train_score                          0.99   \n",
       "split4_test_score                           0.95   \n",
       "split4_train_score                          0.99   \n",
       "std_fit_time                             2.2e-05   \n",
       "std_score_time                           5.9e-06   \n",
       "std_test_score                             0.022   \n",
       "std_train_score                           0.0055   \n",
       "\n",
       "                                               40  \\\n",
       "mean_fit_time                             0.00036   \n",
       "mean_score_time                           0.00021   \n",
       "mean_test_score                              0.96   \n",
       "mean_train_score                             0.99   \n",
       "param_C                                        10   \n",
       "param_gamma                                   NaN   \n",
       "param_kernel                               linear   \n",
       "params              {'C': 10, 'kernel': 'linear'}   \n",
       "rank_test_score                                 3   \n",
       "split0_test_score                            0.96   \n",
       "split0_train_score                           0.99   \n",
       "split1_test_score                               1   \n",
       "split1_train_score                           0.99   \n",
       "split2_test_score                               1   \n",
       "split2_train_score                           0.98   \n",
       "split3_test_score                            0.91   \n",
       "split3_train_score                           0.99   \n",
       "split4_test_score                            0.95   \n",
       "split4_train_score                              1   \n",
       "std_fit_time                              1.9e-05   \n",
       "std_score_time                            2.7e-05   \n",
       "std_test_score                              0.034   \n",
       "std_train_score                             0.007   \n",
       "\n",
       "                                                41  \n",
       "mean_fit_time                              0.00037  \n",
       "mean_score_time                             0.0002  \n",
       "mean_test_score                               0.96  \n",
       "mean_train_score                              0.99  \n",
       "param_C                                        100  \n",
       "param_gamma                                    NaN  \n",
       "param_kernel                                linear  \n",
       "params              {'C': 100, 'kernel': 'linear'}  \n",
       "rank_test_score                                  3  \n",
       "split0_test_score                             0.96  \n",
       "split0_train_score                            0.99  \n",
       "split1_test_score                                1  \n",
       "split1_train_score                            0.99  \n",
       "split2_test_score                                1  \n",
       "split2_train_score                            0.99  \n",
       "split3_test_score                             0.91  \n",
       "split3_train_score                               1  \n",
       "split4_test_score                             0.95  \n",
       "split4_train_score                               1  \n",
       "std_fit_time                               3.8e-05  \n",
       "std_score_time                             2.6e-06  \n",
       "std_test_score                               0.034  \n",
       "std_train_score                             0.0055  \n",
       "\n",
       "[23 rows x 42 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# we display the transposed table so that it better fits on the page:\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using different cross-validation strategies with grid-search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nested cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:  [ 0.967  1.     0.967  0.967  1.   ]\n",
      "Mean cross-validation score:  0.98\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5),\n",
    "                         iris.data, iris.target, cv=5)\n",
    "print(\"Cross-validation scores: \", scores)\n",
    "print(\"Mean cross-validation score: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
    "    outer_scores = []\n",
    "    # for each split of the data in the outer cross-validation\n",
    "    # (split method returns indices of training and test part)\n",
    "    for training_samples, test_samples in outer_cv.split(X, y):\n",
    "        # find best parameter using inner cross-validation\n",
    "        best_parms = {}\n",
    "        best_score = -np.inf\n",
    "        # iterate over parameters\n",
    "        for parameters in parameter_grid:\n",
    "            # accumulate score over inner splits\n",
    "            cv_scores = []\n",
    "            # iterate over inner cross-validation\n",
    "            for inner_train, inner_test in inner_cv.split(\n",
    "                    X[training_samples], y[training_samples]):\n",
    "                # build classifier given parameters and training data\n",
    "                clf = Classifier(**parameters)\n",
    "                clf.fit(X[inner_train], y[inner_train])\n",
    "                # evaluate on inner test set\n",
    "                score = clf.score(X[inner_test], y[inner_test])\n",
    "                cv_scores.append(score)\n",
    "            # compute mean score over inner folds\n",
    "            mean_score = np.mean(cv_scores)\n",
    "            if mean_score > best_score:\n",
    "                # if better than so far, remember parameters\n",
    "                best_score = mean_score\n",
    "                best_params = parameters\n",
    "        # build classifier on best parameters using outer training set\n",
    "        clf = Classifier(**best_params)\n",
    "        clf.fit(X[training_samples], y[training_samples])\n",
    "        # evaluate\n",
    "        outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
    "    return np.array(outer_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [ 0.967  1.     0.967  0.967  1.   ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
    "scores = nested_cv(iris.data, iris.target, StratifiedKFold(5),\n",
    "                   StratifiedKFold(5), SVC, ParameterGrid(param_grid))\n",
    "print(\"Cross-validation scores: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### Parallelizing cross-validation and grid search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics and Scoring\n",
    "#### Keep the End Goal in Mind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics for Binary Classification\n",
    "##### Kinds of errors\n",
    "##### Imbalanced datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "\n",
    "digits = load_digits()\n",
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique predicted labels: [False]\n",
      "Test score: 0.90\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "print(\"Unique predicted labels: {}\".format(np.unique(pred_most_frequent)))\n",
    "print(\"Test score: {:.2f}\".format(dummy_majority.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "print(\"Test score: {:.2f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummy score: 0.82\n",
      "logreg score: 0.98\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "dummy = DummyClassifier().fit(X_train, y_train)\n",
    "pred_dummy = dummy.predict(X_test)\n",
    "print(\"dummy score: {:.2f}\".format(dummy.score(X_test, y_test)))\n",
    "\n",
    "logreg = LogisticRegression(C=0.1).fit(X_train, y_train)\n",
    "pred_logreg = logreg.predict(X_test)\n",
    "print(\"logreg score: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion = confusion_matrix(y_test, pred_logreg)\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQg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\nIFIgL0luZm8gMzUgMCBSID4+CnN0YXJ0eHJlZgo4MjQ3CiUlRU9GCg==\n",
      "image/png": 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AAJ6RkAEAAHhGQgYAAOAZCRkAAIBnJGQAAACekZABAAB4xrQXAJDlioqKfIcA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\nnHKKi0QiNYrjrrvuCmy/R48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ThUIhRSKRWknGUsHMNHbsWL3xxhskY3GQkAGoE44//nh9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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde73af4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_confusion_matrix_illustration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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gL3MgMzAgMCBSIC90IDMxIDAgUiAvVCAzMiAwIFIgL3YgMzMgMCBSIC9QIDM0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\nYXJ0eHJlZgo3NzcwCiUlRU9GCg==\n",
      "image/png": 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RunVrDj/8cDp27EiXLl1o2bJl2DErrUqaqXNusZndDzxuZnfhfZUmB+gIHOqcu8I5t93M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\nkpqpiIhIktRMRUREkvT/QZz2B7QFtzcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde7b64710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_binary_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequent class:\n",
      "[[403   0]\n",
      " [ 47   0]]\n",
      "\n",
      "Dummy model:\n",
      "[[361  42]\n",
      " [ 39   8]]\n",
      "\n",
      "Decision tree:\n",
      "[[390  13]\n",
      " [ 24  23]]\n",
      "\n",
      "Logistic Regression\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Most frequent class:\")\n",
    "print(confusion_matrix(y_test, pred_most_frequent))\n",
    "print(\"\\nDummy model:\")\n",
    "print(confusion_matrix(y_test, pred_dummy))\n",
    "print(\"\\nDecision tree:\")\n",
    "print(confusion_matrix(y_test, pred_tree))\n",
    "print(\"\\nLogistic Regression\")\n",
    "print(confusion_matrix(y_test, pred_logreg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Relation to accuracy\n",
    "\\begin{equation}\n",
    "\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{TP} + \\text{TN} + \\text{FP} + \\text{FN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision, recall and f-score\n",
    "\\begin{equation}\n",
    "\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "source": [
    "\\begin{equation}\n",
    "\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\text{F} = 2 \\cdot \\frac{\\text{precision} \\cdot \\text{recall}}{\\text{precision} + \\text{recall}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score most frequent: 0.00\n",
      "f1 score dummy: 0.16\n",
      "f1 score tree: 0.55\n",
      "f1 score logistic regression: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andy/checkout/scikit-learn/sklearn/metrics/classification.py:1134: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(\"f1 score most frequent: {:.2f}\".format(\n",
    "    f1_score(y_test, pred_most_frequent)))\n",
    "print(\"f1 score dummy: {:.2f}\".format(f1_score(y_test, pred_dummy)))\n",
    "print(\"f1 score tree: {:.2f}\".format(f1_score(y_test, pred_tree)))\n",
    "print(\"f1 score logistic regression: {:.2f}\".format(\n",
    "    f1_score(y_test, pred_logreg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   not nine       0.90      1.00      0.94       403\n",
      "       nine       0.00      0.00      0.00        47\n",
      "\n",
      "avg / total       0.80      0.90      0.85       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andy/checkout/scikit-learn/sklearn/metrics/classification.py:1134: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_most_frequent,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   not nine       0.90      0.90      0.90       403\n",
      "       nine       0.16      0.17      0.16        47\n",
      "\n",
      "avg / total       0.82      0.82      0.82       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_dummy,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "   not nine       0.98      1.00      0.99       403\n",
      "       nine       0.95      0.83      0.89        47\n",
      "\n",
      "avg / total       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_logreg,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Taking uncertainty into account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mglearn.datasets import make_blobs\n",
    "X, y = make_blobs(n_samples=(400, 50), centers=2, cluster_std=[7.0, 2],\n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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841ysRcdnLWkeqoJmhaeFRmK7056Ku9CK9U2ApNhFFd5FztsN2+rrOUnNqj/m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\nUiA+PgpzdGFydHhyZWYKNDE1OTcKJSVFT0YK\n",
      "image/png": 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TRGsIBloSXggRyeboIaXgHI48V0TxBp9zKv6ollERm2MAZZeFEImR+DR0EpuG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DxUSD1JSVbefDOV7u4otFo9k7UG7L1Gt9IdrEE4Lc3EqlVtvPHG9fOU0ShDFmVDMesMQ4YkLEHm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde796a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_decision_threshold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.89      0.93       104\n",
      "          1       0.35      0.67      0.46         9\n",
      "\n",
      "avg / total       0.92      0.88      0.89       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_lower_threshold = svc.decision_function(X_test) > -.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90       104\n",
      "          1       0.32      1.00      0.49         9\n",
      "\n",
      "avg / total       0.95      0.83      0.87       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lower_threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision-Recall curves and ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdde7a5db38>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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phlyWEFYuF0wsB8wC0ZZwCiKeBgCffQy1CmVuZHN0cmVhbQplbmRvYmoKMzMg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\nJUVPRgo=\n",
      "image/png": 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Nw4fTaN8+gLp1fSyOTpV3RT3ZsqlMolBKlZopU/aQlJQFwLx58cybFw/YH2bb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\nUko5aVJQSinl9P/Ct0hqWysoWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde7a2a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a similar dataset as before, but with more samples\n",
    "# to get a smoother curve\n",
    "X, y = make_blobs(n_samples=(4000, 500), centers=2, cluster_std=[7.0, 2],\n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))\n",
    "# find threshold closest to zero\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision, recall, label=\"precision recall curve\")\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdde73eaf28>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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Cl41XTBZjGfRE5S7F9EGqs4WehPKifA6y+aghEl2inIEnBgejQDuw57afiVeF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\nZm8gNDIgMCBSID4+CnN0YXJ0eHJlZgoxMjgzMgolJUVPRgo=\n",
      "image/png": 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zcXKCQYMacuLE5TK/dE4rwVq2vrYgKjZpKQjhaElJkJ5u3NcaXn4ZUlIAWJjZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde7425550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# RandomForestClassifier has predict_proba, but not decision_function\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(\n",
    "    y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(precision, recall, label=\"svc\")\n",
    "\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero svc\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision_rf, recall_rf, label=\"rf\")\n",
    "\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(precision_rf[close_default_rf], recall_rf[close_default_rf], '^', c='k',\n",
    "         markersize=10, label=\"threshold 0.5 rf\", fillstyle=\"none\", mew=2)\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of random forest: 0.610\n",
      "f1_score of svc: 0.656\n"
     ]
    }
   ],
   "source": [
    "print(\"f1_score of random forest: {:.3f}\".format(\n",
    "    f1_score(y_test, rf.predict(X_test))))\n",
    "print(\"f1_score of svc: {:.3f}\".format(f1_score(y_test, svc.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision of random forest: 0.660\n",
      "Average precision of svc: 0.666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "ap_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "ap_svc = average_precision_score(y_test, svc.decision_function(X_test))\n",
    "print(\"Average precision of random forest: {:.3f}\".format(ap_rf))\n",
    "print(\"Average precision of svc: {:.3f}\".format(ap_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Receiver Operating Characteristics (ROC) and AUC\n",
    "\\begin{equation}\n",
    "\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdde79dde10>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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u6HZwzrzDM4w5AhfFWnCm05n2XNjknAcnEM5tlPGMQrpJVBVxVJ9xTPGqss+N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\n",
      "image/png": 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ERAIKBRERCSgUREQkEGoomNlMM9thZrvMbGEL++eY2Stm9qqZrTez/DDrAbjqqnVhv4WI\nSNoKLRTMLAtYDFwJjARmm9nIZs3eAi5x99HA3cDSsOppsmpVPQCzZmWH/VYiImknzJ7CJGCXu1e7\n+4fACqAotoG7r3f3A9GXG4DBIdZznKefnpqstxIRSRthhsIgoDbmdV1028l8CfhdiPWIiEgc3VNd\nAICZTSMSClNOsn8uMBdgyJAhSaxMRKRrCbOnsBvIiXk9OLrtOGY2BvgpUOTu+1s6kLsvdfcCdy8Y\nMGBAKMWKiEi4obARGGZmQ83sNOCLQGVsAzMbAvwW+Hd33xliLSIikoDQho/cvcHMSoFngCxgmbtv\nM7NbovuXAP8J9AceNjOABncvCKsmERFpXahzCu6+CljVbNuSmO+/DHw5zBpERCRxuqNZREQCCgUR\nEQkoFEREJKBQEBGRQJcKBS2GJyLSulPijuZk0WJ4Iu1z9OhR6urq+OCDD1JdisTRs2dPBg8eTI8e\nPdr1810qFJpoMTyRtqmrq+OMM84gNzeX6D1Fcgpyd/bv309dXR1Dhw5t1zG61PCRiLTPBx98QP/+\n/RUIpzgzo3///h3q0SkURCQhCoT00NHfk0JBRNJCVlYWY8eOJS8vj89//vMcPHgw2Ldt2zamT5/O\niBEjGDZsGHfffTfuHuz/3e9+R0FBASNHjmTcuHF885vfbPE9Em2XyRQKIpIWevXqxebNm9m6dStn\nnnkmixcvBuDIkSMUFhaycOFCduzYwZYtW1i/fj0PP/wwAFu3bqW0tJRf/epXbN++naqqKj796U+f\ncPxE251MQ0ND55xoiikURCTtXHTRRezeHVmJ/ze/+Q2TJ0/miiuuAKB3794sWrSIe++9F4D77ruP\nb3/725x33nlApMfxta997YRjttbuxhtvpKysLGh7+umnA/DnP/+ZqVOnUlhYyMiRI1m4cGEQVgDf\n+c53+OEPfwjA/fffz8SJExkzZgx33XVXp/57dKYuefWRiLSf2ROhHNf92oTaHTt2jD/96U986Utf\nAiJDRxMmTDiuzbnnnsuhQ4f4xz/+wdatWxMaBkq0XXN/+ctf2Lp1K0OHDuXll1/mG9/4BvPmzQPg\n8ccf55lnnmH16tW88cYbvPTSS7g7hYWFrF27losvvrjN7xc29RREJC0cOXKEsWPHkp2dzTvvvMPl\nl1+e6pIAmDRpUnD557hx49i7dy979uxhy5Yt9OvXj5ycHFavXs3q1asZN24c48eP5/XXX+eNN95I\nceUtU09BRNok0U/0na1pTuGf//wnM2bMYPHixdx6662MHDmStWvXHte2urqa008/nT59+jBq1Cg2\nbdpEfn5+q8dvrV337t1pbGwEoLGxkQ8//DDY9/GPf/y4ttdeey1lZWXU19dz/fXXA5H7B26//Xa+\n+tWvtuvck0k9BRFJK7179+bBBx/kRz/6EQ0NDcyZM4fnnnuOP/7xj0CkR3HrrbfyrW99C4D58+fz\n/e9/n507Iw93bGxsZMmSJScct7V2ubm5bNq0CYDKykqOHj160vquv/56VqxYQVlZGddeGwnQGTNm\nsGzZMg4dOgTA7t272bt3b2f8c3Q6hYKIpJ1x48YxZswYli9fTq9evaioqOB73/seI0aMYPTo0Uyc\nOJHS0lIAxowZwwMPPMDs2bM5//zzycvLo7q6+oRjttbuK1/5CmvWrCE/P58XXnjhhN5BrFGjRvH+\n++8zaNAgzj77bACuuOIKbrjhBi666CJGjx7NNddcw/vvvx/Cv0zHWey1vOmgoKDAq6qq2vWzTRNk\nqer+iqSr1157jfPPPz/VZUiCWvp9mdmmRB53rJ6CiIgEFAoiIhJQKIiISEChICIiAd2nICKdrqam\nhsrKSg4ePEjfvn0pKioiJycn1WVJAhQKItJp6uvrKS0tpby8PLjZC+C2226juLiYRYsWkZ2tJx+e\nyjR8JCKdor6+nsmTJ7Ny5UqysrIoKSnhjjvuoKSkhG7durFy5UomT57MO++80+ZjHzx4MFj1FCIL\n0X3uc5/rzPIBePTRR4P7GxKVm5vLu+++e8L22MXw0olCQUQ6RWlpKdXV1YwfP57q6mrKysq45557\nKCsr46233gq2Ny0W1xbNQyFRx44da/PPnKqStTS3QkFEOqympoby8nJ69OhBRUUFgwcPPm7/4MGD\nefLJJ+nevTvl5eXU1ta26fgLFy7kzTffZOzYscyfPx+AQ4cOcc0113DeeecxZ86c4KE6ubm5LFiw\ngPHjx/PEE0/w5ptvMnPmTCZMmMDUqVN5/fXXAXjiiSfIy8sjPz//uNVK9+zZw8yZMxk2bFiwVAbA\n8uXLGT16NHl5eSxYsKDFOu+55x6GDx/OlClT2LFjR4ttxo4dG3z16tWLNWvWcPjwYW6++WYmTZrE\nuHHjqKioACI9l8LCQqZPn85ll12GuzN//nzy8vIYPXo0jz32WJv+HRPi7mn1NWHCBG8veNzh8Xb/\nvEhXtX379lb3P/TQQw54SUlJq+1KSkoc8EWLFrXp/d966y0fNWpU8PrZZ5/1Pn36eG1trR87dswv\nvPBCX7dunbu7f+pTn/If/OAHQdvp06f7zp073d19w4YNPm3aNHd3z8vL87q6Ond3P3DggLu7P/LI\nIz506FA/ePCgHzlyxIcMGeI1NTW+e/duz8nJ8b179/rRo0d92rRpXl5eHrzfvn37vKqqyvPy8vzw\n4cP+3nvv+bnnnuv333//Sc+psrLSp0yZ4h9++KHffvvt/stf/jKoZdiwYX7o0CF/5JFHfNCgQb5/\n/353dy8rK/PPfvaz3tDQ4PX19Z6Tk+N79uw54dgt/b6AKk/gb6wmmkWkw5oejTlixIhW2w0fPhyA\nAwcOdPg9J02aFPRIxo4dy1//+lemTJkCEKxOeujQIdavXx8sTAfwr3/9C4DJkydz4403ct111/GF\nL3wh2H/ZZZfxiU98AoCRI0fy9ttvs3//fi699FIGDBgAwJw5c1i7di1XX3118HPr1q2juLiY3r17\nA1BYWHjS2t944w3mz5/Ps88+S48ePVi9ejWVlZXBHMQHH3xATU0NAJdffjlnnnkmAM899xyzZ88m\nKyuLgQMHcskll7Bx48ZW36utFAoi0mF9+/YFOOmQSZOmFUj79evX4ff82Mc+FnyflZV13Jh704J1\njY2N9O3bl82bN5/w80uWLOHFF1/k6aefZsKECcEqqK0dtzMcOnSI6667jp/85CfBgnnuzsqVK08I\n1RdffLHVxffCoDkFEemwwsJCunXrRmVlJXV1dS22qa2tpaKigm7durX5k+0ZZ5zRrlVF+/Tpw9Ch\nQ3niiabFMJ0tW7YA8Oabb3LBBRfw3e9+lwEDBrQ6zzFp0iTWrFnDu+++y7Fjx1i+fDmXXHLJcW0u\nvvhinnzySY4cOcL777/PU0891eKxbr75Zm666SamTp0abJsxYwYPPfRQMC/y8ssvt/izU6dO5bHH\nHuPYsWPs27ePtWvXMmnSpMT/QRIQaiiY2Uwz22Fmu8xsYQv7zcwejO5/xczGh1mPiIRjyJAhFBcX\nc/ToUYqKik74A1tbW8vVV19NQ0MDxcXFbb6RrX///kyePJm8vLxgojlRv/71r/nZz35Gfn4+o0aN\nCiZx58+fH0wcf+Yzn2n1ITxnn3029957L9OmTSM/P58JEyZQVFR0XJvx48dz/fXXk5+fz5VXXsnE\niRNPOM7bb79NWVkZy5YtCyabq6qquPPOOzl69Chjxoxh1KhR3HnnnS3WUVxczJgxY8jPz2f69Onc\nd999nX7fR2hLZ5tZFrATuByoAzYCs919e0ybWcDXgVnABcCP3f2C1o7b3qWzY58rq6WzRdomkaWz\nm+5TqK6upnv37hQVFTF8+HB27txJRUUFDQ0NnHPOOaxfv56BAwcmqfKuqSNLZ4c5pzAJ2OXu1dGC\nVgBFwPaYNkXAL6Iz4xvMrK+Zne3ufwurqFmzdDelSBiys7N5/vnngzuaV65cGezr1q0bJSUlLF68\nWIFwigszFAYBsX3IOiK9gXhtBgGdHgrqHYiELzs7m7KyMmpra6msrOTAgQP069ePwsJCrX2UJtLi\n6iMzmwvMhcjYpYic2nJyctp157KkXpgTzbuB2I8Gg6Pb2toGd1/q7gXuXtB0nbCIJFdY84/SuTr6\newozFDYCw8xsqJmdBnwRqGzWphL4j+hVSBcC74U5nyAi7dOzZ0/279+vYDjFuTv79++nZ8+e7T5G\naMNH7t5gZqXAM0AWsMzdt5nZLdH9S4BVRK482gX8E7gprHpEpP0GDx5MXV0d+/btS3UpEkfPnj1P\nWHuqLUK7JDUs7b0kVUSkK0v0klTd0SwiIgGFgoiIBBQKIiISSLs5BTPbB7zdzh8/CzjxuXmZTefc\nNeicu4aOnPOn3D3uNf1pFwodYWZViUy0ZBKdc9egc+4aknHOGj4SEZGAQkFERAJdLRSWprqAFNA5\ndw06564h9HPuUnMKIiLSuq7WUxARkVZkZCh0xceAJnDOc6Ln+qqZrTezkz97ME3EO+eYdhPNrMHM\nrklmfWFI5JzN7FIz22xm28xsTbJr7GwJ/N/+hJk9ZWZbouec1muomdkyM9trZltPsj/cv1/unlFf\nRBbfexM4BzgN2AKMbNZmFvA7wIALgRdTXXcSzvkzQL/o91d2hXOOaff/iCy+eE2q607C77kvkacb\nDom+/mSq607COd8B/CD6/QDg78Bpqa69A+d8MTAe2HqS/aH+/crEnkLwGFB3/xBoegxorOAxoO6+\nAehrZmcnu9BOFPec3X29ux+IvtxA5NkV6SyR3zNEngG+EtibzOJCksg53wD81t1rANw93c87kXN2\n4AwzM+B0IqHQkNwyO4+7ryVyDicT6t+vTAyFkz3is61t0klbz+dLRD5ppLO452xmg4Bi4L+SWFeY\nEvk9Dwf6mdmfzWyTmf1H0qoLRyLnvAg4H9gDvArc5u6NySkvJUL9+5UWj+OUzmNm04iEwpRU15IE\nDwAL3L0x8iGyS+gOTAAuA3oBL5jZBnffmdqyQjUD2AxMB84F/mBm69z9H6ktKz1lYih02mNA00hC\n52NmY4CfAle6+/4k1RaWRM65AFgRDYSzgFlm1uDuTyanxE6XyDnXAfvd/TBw2MzWAvlAuoZCIud8\nE3CvRwbcd5nZW8B5wEvJKTHpQv37lYnDR13xMaBxz9nMhgC/Bf49Qz41xj1ndx/q7rnunguUAf8r\njQMBEvu/XQFMMbPuZtYbuAB4Lcl1dqZEzrmGSM8IMxsIjACqk1plcoX69yvjegreBR8DmuA5/yfQ\nH3g4+sm5wdN4MbEEzzmjJHLO7v6amf0eeAVoBH7q7i1e2pgOEvw93w08amavErkiZ4G7p+3qqWa2\nHLgUOMvM6oC7gB6QnL9fuqNZREQCmTh8JCIi7aRQEBGRgEJBREQCCgUREQkoFEREJKBQEEmQmR2L\nrj7a9JUbXZH0vejr18zsrmjb2O2vm9kPU12/SCIy7j4FkRAdcfexsRvMLBdY5+6fM7OPA5vN7Kno\n7qbtvYCXzazc3Z9PbskibaOegkgniS4tsQn4dLPtR4iszZPOiy5KF6FQEElcr5iho/LmO82sP5H1\n7bc1294PGAasTU6ZIu2n4SORxJ0wfBQ11cxeJrKsxL3RZRgujW7fQiQQHnD3+iTWKtIuCgWRjlvn\n7p872XYzGwpsMLPH3X1zsosTaQsNH4mEzN3fAu4FFqS6FpF4FAoiybEEuDh6tZLIKUurpIqISEA9\nBRERCSgUREQkoFAQEZGAQkFERAIKBRERCSgUREQkoFAQEZGAQkFERAL/H1+Ovo7TMA3LAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde7a9f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc.decision_function(X_test))\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "# find threshold closest to zero\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdde74d4cf8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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XVQUytgnmYDuZCacoIhC/oCS5ibto6iiOZsaa24WGkiL33T2cnPS6v5xEBV4J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\n",
      "image/png": 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4srKyWLp0aahDM8YYVwSaFB7AqYN0P87VQj8EbncrqPrC4/Ewb6tz/94jDz/MI488AsC8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde737dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve SVC\")\n",
    "plt.plot(fpr_rf, tpr_rf, label=\"ROC Curve RF\")\n",
    "\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero SVC\", fillstyle=\"none\", c='k', mew=2)\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(fpr_rf[close_default_rf], tpr[close_default_rf], '^', markersize=10,\n",
    "         label=\"threshold 0.5 RF\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for Random Forest: 0.937\n",
      "AUC for SVC: 0.916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "svc_auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "print(\"AUC for Random Forest: {:.3f}\".format(rf_auc))\n",
    "print(\"AUC for SVC: {:.3f}\".format(svc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 1.00  accuracy = 0.90  AUC = 0.50\n",
      "gamma = 0.05  accuracy = 0.90  AUC = 1.00\n",
      "gamma = 0.01  accuracy = 0.90  AUC = 1.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdde7477b00>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5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\nL0luZm8gMzIgMCBSID4+CnN0YXJ0eHJlZgo4MDE5CiUlRU9GCg==\n",
      "image/png": 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c5L77EnjzzV8AiIyszNy5UbRqpeCcnBtfHnq6FNhirU0EMMa8DdwArM+2zL+AD6y1vwBY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde73fbba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for gamma in [1, 0.05, 0.01]:\n",
    "    svc = SVC(gamma=gamma).fit(X_train, y_train)\n",
    "    accuracy = svc.score(X_test, y_test)\n",
    "    auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "    fpr, tpr, _ = roc_curve(y_test , svc.decision_function(X_test))\n",
    "    print(\"gamma = {:.2f}  accuracy = {:.2f}  AUC = {:.2f}\".format(\n",
    "        gamma, accuracy, auc))\n",
    "    plt.plot(fpr, tpr, label=\"gamma={:.3f}\".format(gamma))\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlim(-0.01, 1)\n",
    "plt.ylim(0, 1.02)\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi-class classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.953\n",
      "Confusion matrix:\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  2  0  2  0]\n",
      " [ 0  0 41  3  0  0  0  0  0  0]\n",
      " [ 0  0  1 43  0  0  0  0  0  1]\n",
      " [ 0  0  0  0 38  0  0  0  0  0]\n",
      " [ 0  1  0  0  0 47  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  1  0  1  1  0  0 45  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  1  0  1  0  0  1 44]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target, random_state=0)\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)\n",
    "print(\"Accuracy: {:.3f}\".format(accuracy_score(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion_matrix(y_test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
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9O5qx6RqbOOx+70bWyoyuaCF+yFcn6yVg3FMmRRJkTrZYbovVnu6hKKZzhnMZ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\nIDQ2IDAgUiA+PgpzdGFydHhyZWYKMTM2NjIKJSVFT0YK\n",
      "image/png": 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//OMfef3118nJyaFDhw6MGTOm3Fqu/LrcD67DYKsDW4EmqvpVWe1dhMHasGt3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdde738a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_image = mglearn.tools.heatmap(\n",
    "    confusion_matrix(y_test, pred), xlabel='Predicted label',\n",
    "    ylabel='True label', xticklabels=digits.target_names,\n",
    "    yticklabels=digits.target_names, cmap=plt.cm.gray_r, fmt=\"%d\")\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        37\n",
      "          1       0.89      0.91      0.90        43\n",
      "          2       0.95      0.93      0.94        44\n",
      "          3       0.90      0.96      0.92        45\n",
      "          4       0.97      1.00      0.99        38\n",
      "          5       0.98      0.98      0.98        48\n",
      "          6       0.96      1.00      0.98        52\n",
      "          7       1.00      0.94      0.97        48\n",
      "          8       0.93      0.90      0.91        48\n",
      "          9       0.96      0.94      0.95        47\n",
      "\n",
      "avg / total       0.95      0.95      0.95       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro average f1 score: 0.953\n",
      "Macro average f1 score: 0.954\n"
     ]
    }
   ],
   "source": [
    "print(\"Micro average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"micro\")))\n",
    "print(\"Macro average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"macro\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using evaluation metrics in model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default scoring: [ 0.9  0.9  0.9]\n",
      "Explicit accuracy scoring: [ 0.9  0.9  0.9]\n",
      "AUC scoring: [ 0.994  0.99   0.996]\n"
     ]
    }
   ],
   "source": [
    "# default scoring for classification is accuracy\n",
    "print(\"Default scoring: {}\".format(\n",
    "    cross_val_score(SVC(), digits.data, digits.target == 9)))\n",
    "# providing scoring=\"accuracy\" doesn't change the results\n",
    "explicit_accuracy =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                                     scoring=\"accuracy\")\n",
    "print(\"Explicit accuracy scoring: {}\".format(explicit_accuracy))\n",
    "roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                           scoring=\"roc_auc\")\n",
    "print(\"AUC scoring: {}\".format(roc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid-Search with accuracy\n",
      "Best parameters: {'gamma': 0.0001}\n",
      "Best cross-validation score (accuracy)): 0.970\n",
      "Test set AUC: 0.992\n",
      "Test set accuracy: 0.973\n",
      "\n",
      "Grid-Search with AUC\n",
      "Best parameters: {'gamma': 0.01}\n",
      "Best cross-validation score (AUC): 0.997\n",
      "Test set AUC: 1.000\n",
      "Test set accuracy: 1.000\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target == 9, random_state=0)\n",
    "\n",
    "# we provide a somewhat bad grid to illustrate the point:\n",
    "param_grid = {'gamma': [0.0001, 0.01, 0.1, 1, 10]}\n",
    "# using the default scoring of accuracy:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Grid-Search with accuracy\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (accuracy)): {:.3f}\".format(grid.best_score_))\n",
    "print(\"Test set AUC: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Test set accuracy: {:.3f}\".format(grid.score(X_test, y_test)))\n",
    "\n",
    "# using AUC scoring instead:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, scoring=\"roc_auc\")\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"\\nGrid-Search with AUC\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (AUC): {:.3f}\".format(grid.best_score_))\n",
    "print(\"Test set AUC: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Test set accuracy: {:.3f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available scorers:\n",
      "['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'completeness_score', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'mutual_info_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'normalized_mutual_info_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'v_measure_score']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "print(\"Available scorers:\\n{}\".format(sorted(SCORERS.keys())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Outlook"
   ]
  }
 ],
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